Image Orientation Detection via Luminance Region Analysis
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Solution Overview
Problem
Low-end digital cameras and camera phones lack efficient methods for automatic image orientation detection, as existing algorithms are too complex for implementation and require high computational complexity and memory, which is not feasible due to cost and resource constraints.
Innovation Solution
A method that computes average and standard deviation of luminance in specific image regions and uses consolidated luminance differences and uniformity to determine image orientation, utilizing a digital signal processor with low computation and memory requirements, allowing for automatic orientation detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image orientation algorithms are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The image is divided into multiple regions (top, bottom, left, right portions) for separate luminance analysis. This segmentation allows the algorithm to process local region characteristics independently, reducing overall computational complexity while maintaining detection accuracy through regional comparison.
Solution Approach 2:
The algorithm transforms the orientation detection problem into a parameter comparison task by calculating luminance differences and standard deviations for different image regions. By changing the approach from complex feature analysis to simple statistical parameter comparison, the computational burden is significantly reduced while preserving measurement precision.
2Measurement precision
If complex image orientation algorithms are used, then measurement precision is improved, but computation complexity increases
Solution Approach 1:
The algorithm extracts only the essential luminance characteristics from image regions, discarding unnecessary complex features. By taking out only the relevant luminance parameters (average luminance and standard deviation) needed for orientation detection, the computation complexity is reduced while maintaining sufficient measurement precision.
Solution Approach 2:
The approach changes from complex image processing to simple statistical parameter calculation. By computing only luminance averages and standard deviations for each region and comparing these parameters, the algorithm achieves accurate orientation detection with minimal computational requirements.
3Measurement precision
If complex image orientation algorithms are used, then measurement precision is improved, but memory usage increases
Solution Approach 1:
The algorithm extracts and processes only the minimal necessary data - luminance values from selected image regions. By taking out only the essential luminance parameters and discarding redundant information, memory usage is minimized while maintaining sufficient precision for orientation detection.
Solution Approach 2:
The algorithm processes only portions of the image (top, bottom, left, right regions) rather than analyzing the entire image in detail. This partial action approach reduces memory requirements by focusing computational resources on specific critical regions sufficient for orientation determination.
Data Source
AI summary
A method and apparatus for automatically deciding on the orientation of image. The method includes computing average and standard deviation of luminance of the top region of a portion of the image, of the bottom region of a portion of the image, of the left region of a portion of the image, and of the right region of a portion of the image, computing average luminance of the image, computing, in the digital signal processor, consolidated luminance difference and uniformity of top and bottom regions, and left and right regions, utilizing the computed average and standard deviation of at least one of the bottom region, the left region or the right region and utilizing portrait orientation if difLR−difTB>t1&& stdTB−stdLR>t2 or if difLR−difTB>t3&& stdTB−stdLR>t4, otherwise, utilizing landscape orientation.